{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\cross_validation.py:44: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. Also note that the interface of the new CV iterators are different from that of this module. This module will be removed in 0.20.\n",
      "  \"This module will be removed in 0.20.\", DeprecationWarning)\n",
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\grid_search.py:43: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the refactored classes and functions are moved. This module will be removed in 0.20.\n",
      "  DeprecationWarning)\n",
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\learning_curve.py:23: DeprecationWarning: This module was deprecated in version 0.18 in favor of the model_selection module into which all the functions are moved. This module will be removed in 0.20\n",
      "  DeprecationWarning)\n"
     ]
    }
   ],
   "source": [
    "from pandas import Series, DataFrame\n",
    "import numpy as np \n",
    "import pandas as pd \n",
    "from sklearn.metrics import r2_score\n",
    "import matplotlib.pyplot as plt\n",
    "import seaborn as sns\n",
    "from sklearn import linear_model\n",
    "from sklearn import cross_validation\n",
    "#color = sns.color_palette()\n",
    "%matplotlib inline"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>instant</th>\n",
       "      <th>dteday</th>\n",
       "      <th>season</th>\n",
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       "      <th>windspeed</th>\n",
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       "      <th>cnt</th>\n",
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       "  </thead>\n",
       "  <tbody>\n",
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       "      <th>2</th>\n",
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       "      <td>2011-01-03</td>\n",
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       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2011-01-07</td>\n",
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       "      <td>959</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "5        6  2011-01-06       1   0     1        0        4           1   \n",
       "6        7  2011-01-07       1   0     1        0        5           1   \n",
       "7        8  2011-01-08       1   0     1        0        6           0   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "5           1  0.204348  0.233209  0.518261   0.089565      88        1518   \n",
       "6           2  0.196522  0.208839  0.498696   0.168726     148        1362   \n",
       "7           2  0.165000  0.162254  0.535833   0.266804      68         891   \n",
       "\n",
       "    cnt  \n",
       "0   985  \n",
       "1   801  \n",
       "2  1349  \n",
       "3  1562  \n",
       "4  1600  \n",
       "5  1606  \n",
       "6  1510  \n",
       "7   959  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# load data\n",
    "dpath = './Desktop/Data/bikesharing/'\n",
    "data = pd.read_csv(dpath +\"day.csv\")\n",
    "data.head(8)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'pandas.core.frame.DataFrame'>\n",
      "RangeIndex: 731 entries, 0 to 730\n",
      "Data columns (total 16 columns):\n",
      "instant       731 non-null int64\n",
      "dteday        731 non-null object\n",
      "season        731 non-null int64\n",
      "yr            731 non-null int64\n",
      "mnth          731 non-null int64\n",
      "holiday       731 non-null int64\n",
      "weekday       731 non-null int64\n",
      "workingday    731 non-null int64\n",
      "weathersit    731 non-null int64\n",
      "temp          731 non-null float64\n",
      "atemp         731 non-null float64\n",
      "hum           731 non-null float64\n",
      "windspeed     731 non-null float64\n",
      "casual        731 non-null int64\n",
      "registered    731 non-null int64\n",
      "cnt           731 non-null int64\n",
      "dtypes: float64(4), int64(11), object(1)\n",
      "memory usage: 91.5+ KB\n"
     ]
    }
   ],
   "source": [
    "data.info()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "pandas.core.series.Series"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "type(data.dteday)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [],
   "source": [
    "# 标记dteday对应的第几月第几天\n",
    "data['month'] = pd.DatetimeIndex(data.dteday).month\n",
    "data['day'] = pd.DatetimeIndex(data.dteday).dayofweek"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "      <th>0</th>\n",
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       "      <td>2</td>\n",
       "      <td>2011-01-02</td>\n",
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       "      <td>6</td>\n",
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       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>2011-01-03</td>\n",
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       "      <td>1349</td>\n",
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       "      <th>3</th>\n",
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       "      <td>2011-01-04</td>\n",
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       "      <td>0.212122</td>\n",
       "      <td>0.590435</td>\n",
       "      <td>0.160296</td>\n",
       "      <td>108</td>\n",
       "      <td>1454</td>\n",
       "      <td>1562</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>2011-01-05</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1</td>\n",
       "      <td>0.226957</td>\n",
       "      <td>0.229270</td>\n",
       "      <td>0.436957</td>\n",
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       "      <td>1518</td>\n",
       "      <td>1600</td>\n",
       "      <td>1</td>\n",
       "      <td>2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>2011-01-06</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>4</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.204348</td>\n",
       "      <td>0.233209</td>\n",
       "      <td>0.518261</td>\n",
       "      <td>0.089565</td>\n",
       "      <td>88</td>\n",
       "      <td>1518</td>\n",
       "      <td>1606</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>2011-01-07</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
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       "      <td>1362</td>\n",
       "      <td>1510</td>\n",
       "      <td>1</td>\n",
       "      <td>4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2011-01-08</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.165000</td>\n",
       "      <td>0.162254</td>\n",
       "      <td>0.535833</td>\n",
       "      <td>0.266804</td>\n",
       "      <td>68</td>\n",
       "      <td>891</td>\n",
       "      <td>959</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>2011-01-09</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0.138333</td>\n",
       "      <td>0.116175</td>\n",
       "      <td>0.434167</td>\n",
       "      <td>0.361950</td>\n",
       "      <td>54</td>\n",
       "      <td>768</td>\n",
       "      <td>822</td>\n",
       "      <td>1</td>\n",
       "      <td>6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>2011-01-10</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.150833</td>\n",
       "      <td>0.150888</td>\n",
       "      <td>0.482917</td>\n",
       "      <td>0.223267</td>\n",
       "      <td>41</td>\n",
       "      <td>1280</td>\n",
       "      <td>1321</td>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   instant      dteday  season  yr  mnth  holiday  weekday  workingday  \\\n",
       "0        1  2011-01-01       1   0     1        0        6           0   \n",
       "1        2  2011-01-02       1   0     1        0        0           0   \n",
       "2        3  2011-01-03       1   0     1        0        1           1   \n",
       "3        4  2011-01-04       1   0     1        0        2           1   \n",
       "4        5  2011-01-05       1   0     1        0        3           1   \n",
       "5        6  2011-01-06       1   0     1        0        4           1   \n",
       "6        7  2011-01-07       1   0     1        0        5           1   \n",
       "7        8  2011-01-08       1   0     1        0        6           0   \n",
       "8        9  2011-01-09       1   0     1        0        0           0   \n",
       "9       10  2011-01-10       1   0     1        0        1           1   \n",
       "\n",
       "   weathersit      temp     atemp       hum  windspeed  casual  registered  \\\n",
       "0           2  0.344167  0.363625  0.805833   0.160446     331         654   \n",
       "1           2  0.363478  0.353739  0.696087   0.248539     131         670   \n",
       "2           1  0.196364  0.189405  0.437273   0.248309     120        1229   \n",
       "3           1  0.200000  0.212122  0.590435   0.160296     108        1454   \n",
       "4           1  0.226957  0.229270  0.436957   0.186900      82        1518   \n",
       "5           1  0.204348  0.233209  0.518261   0.089565      88        1518   \n",
       "6           2  0.196522  0.208839  0.498696   0.168726     148        1362   \n",
       "7           2  0.165000  0.162254  0.535833   0.266804      68         891   \n",
       "8           1  0.138333  0.116175  0.434167   0.361950      54         768   \n",
       "9           1  0.150833  0.150888  0.482917   0.223267      41        1280   \n",
       "\n",
       "    cnt  month  day  \n",
       "0   985      1    5  \n",
       "1   801      1    6  \n",
       "2  1349      1    0  \n",
       "3  1562      1    1  \n",
       "4  1600      1    2  \n",
       "5  1606      1    3  \n",
       "6  1510      1    4  \n",
       "7   959      1    5  \n",
       "8   822      1    6  \n",
       "9  1321      1    0  "
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "data = data.drop(['dteday', 'casual', 'registered','instant','mnth'], axis = 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>6</td>\n",
       "      <td>0</td>\n",
       "      <td>2</td>\n",
       "      <td>0.344167</td>\n",
       "      <td>0.363625</td>\n",
       "      <td>0.805833</td>\n",
       "      <td>0.160446</td>\n",
       "      <td>985</td>\n",
       "      <td>1</td>\n",
       "      <td>5</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   season  yr  holiday  weekday  workingday  weathersit      temp     atemp  \\\n",
       "0       1   0        0        6           0           2  0.344167  0.363625   \n",
       "\n",
       "        hum  windspeed  cnt  month  day  \n",
       "0  0.805833   0.160446  985      1    5  "
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.head(1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>season</th>\n",
       "      <th>yr</th>\n",
       "      <th>holiday</th>\n",
       "      <th>weekday</th>\n",
       "      <th>workingday</th>\n",
       "      <th>weathersit</th>\n",
       "      <th>temp</th>\n",
       "      <th>atemp</th>\n",
       "      <th>hum</th>\n",
       "      <th>windspeed</th>\n",
       "      <th>cnt</th>\n",
       "      <th>month</th>\n",
       "      <th>day</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>count</th>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "      <td>731.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean</th>\n",
       "      <td>2.496580</td>\n",
       "      <td>0.500684</td>\n",
       "      <td>0.028728</td>\n",
       "      <td>2.997264</td>\n",
       "      <td>0.683995</td>\n",
       "      <td>1.395349</td>\n",
       "      <td>0.495385</td>\n",
       "      <td>0.474354</td>\n",
       "      <td>0.627894</td>\n",
       "      <td>0.190486</td>\n",
       "      <td>4504.348837</td>\n",
       "      <td>6.519836</td>\n",
       "      <td>3.002736</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std</th>\n",
       "      <td>1.110807</td>\n",
       "      <td>0.500342</td>\n",
       "      <td>0.167155</td>\n",
       "      <td>2.004787</td>\n",
       "      <td>0.465233</td>\n",
       "      <td>0.544894</td>\n",
       "      <td>0.183051</td>\n",
       "      <td>0.162961</td>\n",
       "      <td>0.142429</td>\n",
       "      <td>0.077498</td>\n",
       "      <td>1937.211452</td>\n",
       "      <td>3.451913</td>\n",
       "      <td>2.004787</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>min</th>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.059130</td>\n",
       "      <td>0.079070</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.022392</td>\n",
       "      <td>22.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>25%</th>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.337083</td>\n",
       "      <td>0.337842</td>\n",
       "      <td>0.520000</td>\n",
       "      <td>0.134950</td>\n",
       "      <td>3152.000000</td>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>50%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.498333</td>\n",
       "      <td>0.486733</td>\n",
       "      <td>0.626667</td>\n",
       "      <td>0.180975</td>\n",
       "      <td>4548.000000</td>\n",
       "      <td>7.000000</td>\n",
       "      <td>3.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>75%</th>\n",
       "      <td>3.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.000000</td>\n",
       "      <td>5.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>2.000000</td>\n",
       "      <td>0.655417</td>\n",
       "      <td>0.608602</td>\n",
       "      <td>0.730209</td>\n",
       "      <td>0.233214</td>\n",
       "      <td>5956.000000</td>\n",
       "      <td>10.000000</td>\n",
       "      <td>5.000000</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>max</th>\n",
       "      <td>4.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>6.000000</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>3.000000</td>\n",
       "      <td>0.861667</td>\n",
       "      <td>0.840896</td>\n",
       "      <td>0.972500</td>\n",
       "      <td>0.507463</td>\n",
       "      <td>8714.000000</td>\n",
       "      <td>12.000000</td>\n",
       "      <td>6.000000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "           season          yr     holiday     weekday  workingday  weathersit  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000  731.000000  731.000000   \n",
       "mean     2.496580    0.500684    0.028728    2.997264    0.683995    1.395349   \n",
       "std      1.110807    0.500342    0.167155    2.004787    0.465233    0.544894   \n",
       "min      1.000000    0.000000    0.000000    0.000000    0.000000    1.000000   \n",
       "25%      2.000000    0.000000    0.000000    1.000000    0.000000    1.000000   \n",
       "50%      3.000000    1.000000    0.000000    3.000000    1.000000    1.000000   \n",
       "75%      3.000000    1.000000    0.000000    5.000000    1.000000    2.000000   \n",
       "max      4.000000    1.000000    1.000000    6.000000    1.000000    3.000000   \n",
       "\n",
       "             temp       atemp         hum   windspeed          cnt  \\\n",
       "count  731.000000  731.000000  731.000000  731.000000   731.000000   \n",
       "mean     0.495385    0.474354    0.627894    0.190486  4504.348837   \n",
       "std      0.183051    0.162961    0.142429    0.077498  1937.211452   \n",
       "min      0.059130    0.079070    0.000000    0.022392    22.000000   \n",
       "25%      0.337083    0.337842    0.520000    0.134950  3152.000000   \n",
       "50%      0.498333    0.486733    0.626667    0.180975  4548.000000   \n",
       "75%      0.655417    0.608602    0.730209    0.233214  5956.000000   \n",
       "max      0.861667    0.840896    0.972500    0.507463  8714.000000   \n",
       "\n",
       "            month         day  \n",
       "count  731.000000  731.000000  \n",
       "mean     6.519836    3.002736  \n",
       "std      3.451913    2.004787  \n",
       "min      1.000000    0.000000  \n",
       "25%      4.000000    1.000000  \n",
       "50%      7.000000    3.000000  \n",
       "75%     10.000000    5.000000  \n",
       "max     12.000000    6.000000  "
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.describe()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "season        0\n",
       "yr            0\n",
       "holiday       0\n",
       "weekday       0\n",
       "workingday    0\n",
       "weathersit    0\n",
       "temp          0\n",
       "atemp         0\n",
       "hum           0\n",
       "windspeed     0\n",
       "cnt           0\n",
       "month         0\n",
       "day           0\n",
       "dtype: int64"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.isnull().sum()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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po7MBeG37qTBHEnvst94EVVXXzPpdpeRlJXPdtGG9NzAmTPKzUxhdkMHOQxWc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\nBGUJwhhjTFD/H5H0V2lQOv5TAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbecd550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cnt \n",
    "fig = plt.figure()\n",
    "sns.distplot(data.cnt.values, bins=50, kde=True)\n",
    "plt.xlabel('The cnt values of bikesharing', fontsize =12)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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/A7MURWlUFGU8cBSmo3YbMC9jXUEeZNkLE3VAvUxP42RFNbS125oHcoWEVWI4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\nAG+XLhuzYGwiThzDK4ciRD5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iFy8aLcqRkvAlDQ46z15slllpCrnF8rrZJMbIfb3IefpU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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x690e320>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# cnt 散点图\n",
    "plt.scatter(range(data.shape[0]),data[\"cnt\"].values, color='red')\n",
    "plt.title(\"The count of people\");"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x691f2b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# histogram of weathersit \n",
    "fig = plt.figure()\n",
    "sns.distplot(data.weathersit.values, bins = 50 , kde = False)\n",
    "plt.xlabel(\"The influence of weathersit in bikes sharing\", fontsize= 10)\n",
    "plt.ylabel(\"Number of occurrence\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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GtqvrEcAPMvPm+lv8d4E92jVWUqg3Xbpg7kpmXpKZv5yUmjLz7sz8DUBEvBZYQjU0NNa6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\nSSrI/wMsQe1dG62AXgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x690e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# temperature\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.temp.values, bins = 50, kde = False)\n",
    "plt.xlabel(\"Temperature condition\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf08400>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# humidity\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.hum.values, bins = 40, kde = False)\n",
    "plt.xlabel(\"Humidity condition\")\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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RcUtEnNf2/MqI2F3PDTiw7cGI2BoRTzuM424ZxkSRiNgWEQuDPq/Gm+GtUfRF4NS25xuB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\nygfQSTt5AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc07aa20>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# windspeed\n",
    "fig = plt.figure()\n",
    "sns.distplot(data.windspeed.values, bins = 40, kde=False)\n",
    "plt.xlabel('Windspeed')\n",
    "plt.show()# The correlation between the two features\n",
    "cols = data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {},
   "outputs": [],
   "source": [
    "# The correlation between the two features\n",
    "cols = data.columns\n",
    "\n",
    "# Calutates pearson co-efficient for all combination\n",
    "data_corr = data.corr().abs()\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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V83EG9O3FojmzCPAvT8y8BaSlpfHuGx2YOHYUs6Om8eXX36AeO26X7NaM9tuv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xi2EOAPoAF6nqt35TVT4iMh037/H7+G1R3pQmqC99AW76w7O46ht9vYaqZESku6q+6zuHMcYY\nUxLr8Q7fZOA+4GZcjd7hwKk+A1VSG+Kl+pJEL9xGI/9R1ZEiEtmREBEZo6p9d9v8ByDqm/78JCLj\ncZvQpODmyZ7lOZMxCRORdbj3XHXcRlCrgCbAj6ra0mM0Y0xIrOEdvjxcubiBqvqKiER97nFSEZGH\ngr9WE5GpwAySY25eKi5nvCEb2d55Coa1d9/8p3pFBymjp3BD9D1xW7FX8xvHmLJR1UYAIvIicJeq\nrhKRxsATfpMZY8KS6jtAJVQV9+X/iYicin35h02DP88CL+E2mojfFmWv4OqOtxGRd4E3PecpkarG\nd6a8RFVXBCMLtYn+VsobVPVlYKuq3gc09ZzHmH11sKquAlDVtUBzz3mMMSGxHu/w9QHOACYCPYAr\n/capXFT1Od8Z9tGVwBJgDLBAVed4zpOITiJyA1ALuILoV2LJE5GOQHqww6aVEzTJar6IvABMw9Xy\nnu45jzEmJLa40pgKIiLtcVVCegA/qOqFniPtlYik4tYs1AfOjvLiVYCg0d0RVzFmFPCiqtoQvUk6\nwXvvAqAdMF9Vp3iOZIwJiU01MaYCiMhhwNnA6cFNCz3G2SsR+VJEvgA+w5WNOwn4KLgtslR1Hq4e\n7364ykIj/CYyZp9l4DYFWwPsJyJXeM5jjAmJTTUxpmJ8DCzDLbqNesm7+KLKmkC2zyBlUVzJRsBK\nNppkNAVYi6tqArtVFzLGJC9reBtTMQ4EugJnicgdwHpVvdRzpmLFyzSKyGeq2tV3njJImpKNxpQi\nVVV7+w5hjAmfNbyNqRj74+rxtsANIydDDfJMEXkCVzEmD0BVn977IV4lU8lGY/ZmtogcC8ykoFzq\nDr+RjDFhsIa3MRXjPVwJwSHBXORkEJ/T3cBrisTFSza2jHrJRmNKcTJuIXZcDDjYUxZjTIisqokx\npkQicjauUohGvbKCiEzHlWx8jeQp2WiMMeZXxBrexphiicjDuAWKn+HmTi9T1Tv9ptq7ZCvZaExh\nIjJGVfsGJ5FFpkqp6gmeYhljQmRTTYwxJTlJVU8EeX4p7AAAAU1JREFUEJGRwFee8+xVULLxNyRB\nyUZjSvBAcNkKmIrbOOddINNbImNMqKzhbYwpSVURSVXVPCCF6Jc0S6aSjcbsQVV/CC7rBqM35wHP\nAOtxpTKNMUnOGt7GmJL8E/hcRL4CjgVe9ZynNElTstGYvSk0enNacNMCj3GMMSGyhrcxpiSXAMtx\nc7z/lgSLFZOxZKMxxbHRG2MqKVtcaYwpUTItVhSRb3AlBN9IopKNxuxBRKoQjN4Ax2CjN8ZUGtbj\nbYwpVrItVlTVo3xnMCYkNnpjTCVlDW9jTElsuNsYP5Jxwy1jTAJsqokxplg23G2MMcaEK9V3AGNM\nZNlwtzHGGBMim2pijCmJDXcbY4wxIbKpJsYYY4wxxlQAm2pijDHGGGNMBbCGtzHGGGOMMRXAGt7G\nGGOMMcZUAGt4G2OMMcYYUwGs4W2MMcYYY0wF+H9waj+PabNpTgAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbefd780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.subplots(figsize=(13,9))\n",
    "sns.heatmap(data_corr, annot=True)\n",
    "sns.heatmap(data_corr, mask=data_corr<1, cbar = False)\n",
    "plt.savefig('bikesharing_coor.png')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "temp and atemp = 0.99\n",
      "season and month = 0.83\n",
      "workingday and day = 0.70\n",
      "atemp and cnt = 0.63\n",
      "temp and cnt = 0.63\n",
      "weathersit and hum = 0.59\n",
      "yr and cnt = 0.57\n"
     ]
    }
   ],
   "source": [
    "# Set the threshold to select only highly correlated attributer\n",
    "threshold = 0.5\n",
    "#list of pairs along whit correlation above threshold\n",
    "corr_list = []\n",
    "size = data_corr.shape[0]\n",
    "\n",
    "# Search for the highly correlated pairs\n",
    "for i in range(0,size):\n",
    "    for j in range(i+1, size):\n",
    "        if (data_corr.iloc[i,j] >= threshold and data_corr.iloc[i,j] < 1):\n",
    "            corr_list.append([data_corr.iloc[i,j], i, j]) # store correlated\n",
    "# sort to show higher ones first\n",
    "s_corr_list = sorted(corr_list, key = lambda x: -abs(x[0]))\n",
    "\n",
    "# Print correlations and column names\n",
    "for v,i,j in s_corr_list:\n",
    "    print(\"%s and %s = %.2f\" %(cols[i],cols[j], v))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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dgfFZrCUGyu0mevon6Owbx24xLlvcgnfKsHY2OIjPp3iotXpVs1CtVkN3/0TB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\nZzcAAAAASUVORK5CYII=\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc7679b0>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc70fe48>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0x65e6550>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Qi8WlBG84m1gsRDqd5d2k+8yTnfmC8AWFfDYqkYHpc+VKFKEEuru5BqHIcpEf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1gjiLFLglQjZYVYluUg8LsrbYdgbSLGLglSiVTerBMIko4QYUEqIIRYIBAIVhghxAKBQLDCCCEW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\nK8z/D1FusIcrZii7AAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0x65e1b00>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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WLW5OePPmceqgKe83yMVO0qsgREzwQINLaY1Pfc2sU+LZgVbY7P4s0rhq8+CZ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PIoh8d3lMFlz9K9YSd//StHmtDeM4LO3sxb0ssjKRw5FrxypAlt3SMSF0R9ZWHG1RegUPRAhXgN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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc7ecc50>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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7WyAF9JQykFLyvDynB9K+e3ydHkhzYE97pUOUCulpTdHTmqKlpZ6xMdXnJXhr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\nWnvIGLMR+Gtg8U1drnPiRjG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      "text/plain": [
       "<matplotlib.figure.Figure at 0xbf12f60>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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V9YhbU5ojDtT54Unf9JTV5o5FJBwcJ0JnS63vHHFnS22gU5ehHhGvuZc8U9pL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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc05e780>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for v, i, j in s_corr_list:\n",
    "    sns.pairplot(data, size = 5, x_vars = cols[i], y_vars = cols[j])\n",
    "    plt.show()\n",
    "    "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "X shape is: (731, 12)\n",
      "y shape is: (731,)\n"
     ]
    }
   ],
   "source": [
    "# 分割数据\n",
    "y = data['cnt'].values\n",
    "X = data.drop(['cnt'], axis = 1)\n",
    "print('X shape is:', X.shape)\n",
    "print('y shape is:', y.shape)\n",
    "\n",
    "# 为了显示权重系数\n",
    "columns = X.columns\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2011训练数据: (365, 12)\n",
      "2011测试数据: (366, 12)\n",
      "2011训练数据: (365,)\n",
      "2012测试数据: (366,)\n"
     ]
    }
   ],
   "source": [
    "# 取数据中2011年的特征\n",
    "X_2011 = data[data.yr==0]\n",
    "# 取数据中2012年的特征\n",
    "X_2012 = data[data.yr==1]\n",
    "X_train = X[X.yr==0]\n",
    "X_test = X[X.yr==1]\n",
    "y_train = X_2011['cnt'].values\n",
    "y_test = X_2012['cnt'].values\n",
    "print('2011训练数据:', X_train.shape)\n",
    "print('2011测试数据:', X_test.shape)\n",
    "print('2011训练数据:', y_train.shape)\n",
    "print('2012测试数据:', y_test.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "这里的分割感觉有问题。"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\utils\\validation.py:429: DataConversionWarning: Data with input dtype int64 was converted to float64 by StandardScaler.\n",
      "  warnings.warn(msg, _DataConversionWarning)\n"
     ]
    }
   ],
   "source": [
    "#数据标准化\n",
    "from sklearn.preprocessing import StandardScaler\n",
    "\n",
    "#建立标准化生成器\n",
    "ss_X = StandardScaler()\n",
    "ss_y = StandardScaler()\n",
    "\n",
    "# 对训练数据进行标准化\n",
    "X_train = ss_X.fit_transform(X_train)\n",
    "X_test = ss_X.transform(X_test)\n",
    "\n",
    "y_train = ss_y.fit_transform(y_train.reshape(-1,1))\n",
    "y_test = ss_y.transform(y_test.reshape(-1,1))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.655629964983]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.295134324615]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0359658349529]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.00517147419455]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.00394334912287]</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-4.4408920985e-16]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.00263300095055]</td>\n",
       "      <td>day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.0215599423722]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.036923920106]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.059487568978]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.123935941959]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.225692650157]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                   coef     columns\n",
       "6      [0.655629964983]        temp\n",
       "0      [0.295134324615]      season\n",
       "3     [0.0359658349529]     weekday\n",
       "4    [0.00517147419455]  workingday\n",
       "10   [0.00394334912287]       month\n",
       "1   [-4.4408920985e-16]          yr\n",
       "11  [-0.00263300095055]         day\n",
       "7    [-0.0215599423722]       atemp\n",
       "2     [-0.036923920106]     holiday\n",
       "8     [-0.059487568978]         hum\n",
       "9     [-0.123935941959]   windspeed\n",
       "5     [-0.225692650157]  weathersit"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import LinearRegression\n",
    "\n",
    "# 初始化\n",
    "lr = LinearRegression()\n",
    "\n",
    "# 训练模型参数\n",
    "lr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "y_test_pred_lr = lr.predict(X_test)\n",
    "y_train_pred_lr = lr.predict(X_train)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\": list(columns), \"coef\": list((lr.coef_.T))})\n",
    "fs.sort_values(by = ['coef'], ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "temp season weekday workingday权重系数比较高而且也符合逻辑， 但是atemp在这里分析后可以这么低，而且按绝对值的话，yr的权重情况跟实际情况不吻合"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LinearRegression on test is -0.696551159935\n",
      "The r2 score of LinearRegression on train is 0.758521805398\n"
     ]
    }
   ],
   "source": [
    "#测试集\n",
    "print('The r2 score of LinearRegression on test is', r2_score(y_test, y_test_pred_lr))\n",
    "#训练集\n",
    "print('The r2 score of LinearRegression on train is', r2_score(y_train, y_train_pred_lr))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<matplotlib.figure.Figure at 0xc6e6d30>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = plt.subplots(figsize=(7,5))\n",
    "f.tight_layout()\n",
    "ax.hist(y_train - y_train_pred_lr, bins = 50, label='Residuals', color='r', alpha = .6);\n",
    "ax.set_title(\"Histogram of Residuals\")\n",
    "ax.legend(loc= 'best');"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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pmH83qTl5mmxsTn2DnUvOG+gWBD9j89TRvbn4HO1TGZVUQtfF3xLpNX8nSimv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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc56a710>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "#预测值与真值的散点图\n",
    "plt.figure(figsize=(4,3))\n",
    "plt.scatter(y_train, y_train_pred_lr)\n",
    "plt.plot([-3, 3], [-3, 3], '--k')\n",
    "plt.axis('tight')\n",
    "plt.xlabel('True')\n",
    "plt.ylabel('Predicted price')\n",
    "plt.tight_layout()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\utils\\validation.py:526: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "array([ 0.21834714,  0.        , -0.03371701,  0.03251574,  0.00662198,\n",
       "       -0.2086714 ,  0.32460306,  0.32143521, -0.06786622, -0.11945777,\n",
       "        0.08378678,  0.00362541])"
      ]
     },
     "execution_count": 34,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.linear_model import SGDRegressor\n",
    "\n",
    "# 使用默认配置初始化线\n",
    "sgdr = SGDRegressor()\n",
    "\n",
    "# 训练：参数估计\n",
    "sgdr.fit(X_train, y_train)\n",
    "\n",
    "# 预测\n",
    "sgdr_y_predict = sgdr.predict(X_test)\n",
    "sgdr.coef_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The value of default measurement of SGDRegressor on test is -0.69654077044\n",
      "The value of default measurement of SGDRegressor on train is 0.755476109854\n"
     ]
    }
   ],
   "source": [
    "print('The value of default measurement of SGDRegressor on test is', sgdr.score(X_test, y_test))\n",
    "print('The value of default measurement of SGDRegressor on train is', sgdr.score(X_train, y_train))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of RidgeCV on test is -0.701130987727\n",
      "The r2 score of RidgeCV on train is 0.757698223526\n"
     ]
    }
   ],
   "source": [
    "from sklearn.linear_model import RidgeCV\n",
    "\n",
    "# 设置正则参数范围\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "#生成一个RidgeCV实例\n",
    "ridge = RidgeCV(alphas=alphas, store_cv_values=True)\n",
    "\n",
    "#模型训练\n",
    "ridge.fit(X_train, y_train)\n",
    "\n",
    "#预测 \n",
    "y_test_pred_ridge = ridge.predict(X_test)\n",
    "y_train_pred_ridge = ridge.predict(X_train)\n",
    "\n",
    "# r2 score 评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of RidgeCV on test is', r2_score(y_test, y_test_pred_ridge))\n",
    "print('The r2 score of RidgeCV on train is', r2_score(y_train, y_train_pred_ridge))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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avIDotRJ4YlB/QpEB0qyzIoNkZp8Ffumc22tmVwHfcc4tiO1bB/yp\ncy7hGh6xu6H+3Dm3ahhqmg886Jy7e6jHEumPbp0VGby3gP82swjemu2/E7fvD4C/BH5zhGv6Et5t\nuiK+UM9CREQS0jULERFJSGEhIiIJKSxERCQhhYWIiCSksBARkYT+P4rsXp5GFOmQAAAAAElFTkSu\nQmCC\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xbfb2748>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 10.0\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style>\n",
       "    .dataframe thead tr:only-child th {\n",
       "        text-align: right;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: left;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>[0.655629964983]</td>\n",
       "      <td>[0.350160925498]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>[-0.0215599423722]</td>\n",
       "      <td>[0.280455665264]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>[0.295134324615]</td>\n",
       "      <td>[0.275756076852]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>[0.0359658349529]</td>\n",
       "      <td>[0.0341799557374]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>[0.00394334912287]</td>\n",
       "      <td>[0.0188534422286]</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>[0.00517147419455]</td>\n",
       "      <td>[0.00669579815907]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>[-4.4408920985e-16]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>[-0.00263300095055]</td>\n",
       "      <td>[-0.000768661515266]</td>\n",
       "      <td>day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>[-0.036923920106]</td>\n",
       "      <td>[-0.033893233126]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>[-0.059487568978]</td>\n",
       "      <td>[-0.0629670606878]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>[-0.123935941959]</td>\n",
       "      <td>[-0.117141371205]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>[-0.225692650157]</td>\n",
       "      <td>[-0.216428517895]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "                coef_lr            coef_ridge     columns\n",
       "6      [0.655629964983]      [0.350160925498]        temp\n",
       "7    [-0.0215599423722]      [0.280455665264]       atemp\n",
       "0      [0.295134324615]      [0.275756076852]      season\n",
       "3     [0.0359658349529]     [0.0341799557374]     weekday\n",
       "10   [0.00394334912287]     [0.0188534422286]       month\n",
       "4    [0.00517147419455]    [0.00669579815907]  workingday\n",
       "1   [-4.4408920985e-16]                 [0.0]          yr\n",
       "11  [-0.00263300095055]  [-0.000768661515266]         day\n",
       "2     [-0.036923920106]     [-0.033893233126]     holiday\n",
       "8     [-0.059487568978]    [-0.0629670606878]         hum\n",
       "9     [-0.123935941959]     [-0.117141371205]   windspeed\n",
       "5     [-0.225692650157]     [-0.216428517895]  weathersit"
      ]
     },
     "execution_count": 45,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 观察正则参数最佳值\n",
    "mse_mean = np.mean(ridge.cv_values_, axis = 0)\n",
    "plt.plot(np.log10(alphas), mse_mean.reshape(len(alphas), 1))\n",
    "\n",
    "plt.plot(np.log10(ridge.alpha_)*np.ones(3), [0.26, 0.2650, 0.27])\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "print('alpha is:', ridge.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\": list(columns), \"coef_lr\": list((lr.coef_.T)), \"coef_ridge\": list((ridge.coef_.T))})\n",
    "fs.sort_values(by=['coef_ridge'], ascending=False)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "在经过ridgeCV的分析后，temp atemp season是权重最高的三个特征，weathersit也符合逻辑"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "The r2 score of LassoCV on test is  -0.863855024062\n",
      "The r2 score of LassoCV on train is  0.692374913339\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "D:\\Anaconda3\\envs\\py3\\lib\\site-packages\\sklearn\\linear_model\\coordinate_descent.py:1082: DataConversionWarning: A column-vector y was passed when a 1d array was expected. Please change the shape of y to (n_samples, ), for example using ravel().\n",
      "  y = column_or_1d(y, warn=True)\n"
     ]
    }
   ],
   "source": [
    "# 正则化的线性回归（L1正则 --> Lasso)\n",
    "#class sklearn.linear_model.LassoCV(eps=0.001, n_alphas = 100, alphas=None, fit_intercept=True,\n",
    "#                                    normalize=False, precompute='auto'), max_iter = 1000,\n",
    "#                                    tol=0.0001, copy_X=True, cv=Noe, verbose=False, n_jobs=1,\n",
    "#                                    positive=False, random_state=Noe, selection='cyclic')\n",
    "from sklearn.linear_model import LassoCV\n",
    "\n",
    "#设置超参数搜索范围\n",
    "alphas = [0.01, 0.1, 1, 10, 100]\n",
    "\n",
    "#生成一个Lasso实例\n",
    "#lasso = LassoCV(alphas=alphas)\n",
    "lasso = LassoCV()\n",
    "\n",
    "# 训练(内含CV)\n",
    "lasso.fit(X_train, y_train)\n",
    "\n",
    "#训练\n",
    "y_test_pred_lasso = lasso.predict(X_test)\n",
    "y_train_pred_lasso = lasso.predict(X_train)\n",
    "\n",
    "#评估r2_score评价模型在测试集和训练集上的性能\n",
    "print('The r2 score of LassoCV on test is ', r2_score(y_test, y_test_pred_lasso))\n",
    "print('The r2 score of LassoCV on train is ', r2_score(y_train, y_train_pred_lasso))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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B1xsfox7GSztAbRlqG7d/8XS+8YvVPLdmH26PmxsvXhj2cfwjfVz2BGc2O3Xp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\n",
      "text/plain": [
       "<matplotlib.figure.Figure at 0xc76e518>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "alpha is: 0.145275886173\n"
     ]
    },
    {
     "data": {
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       "\n",
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       "        text-align: left;\n",
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       "\n",
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       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>coef_lasso</th>\n",
       "      <th>coef_lr</th>\n",
       "      <th>coef_ridge</th>\n",
       "      <th>columns</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>0.476180</td>\n",
       "      <td>[-0.0215599423722]</td>\n",
       "      <td>[0.280455665264]</td>\n",
       "      <td>atemp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.193429</td>\n",
       "      <td>[0.295134324615]</td>\n",
       "      <td>[0.275756076852]</td>\n",
       "      <td>season</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>0.067640</td>\n",
       "      <td>[0.655629964983]</td>\n",
       "      <td>[0.350160925498]</td>\n",
       "      <td>temp</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-4.4408920985e-16]</td>\n",
       "      <td>[0.0]</td>\n",
       "      <td>yr</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.036923920106]</td>\n",
       "      <td>[-0.033893233126]</td>\n",
       "      <td>holiday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.0359658349529]</td>\n",
       "      <td>[0.0341799557374]</td>\n",
       "      <td>weekday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.00517147419455]</td>\n",
       "      <td>[0.00669579815907]</td>\n",
       "      <td>workingday</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.059487568978]</td>\n",
       "      <td>[-0.0629670606878]</td>\n",
       "      <td>hum</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>-0.000000</td>\n",
       "      <td>[-0.123935941959]</td>\n",
       "      <td>[-0.117141371205]</td>\n",
       "      <td>windspeed</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[0.00394334912287]</td>\n",
       "      <td>[0.0188534422286]</td>\n",
       "      <td>month</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>0.000000</td>\n",
       "      <td>[-0.00263300095055]</td>\n",
       "      <td>[-0.000768661515266]</td>\n",
       "      <td>day</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>-0.127561</td>\n",
       "      <td>[-0.225692650157]</td>\n",
       "      <td>[-0.216428517895]</td>\n",
       "      <td>weathersit</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "    coef_lasso              coef_lr            coef_ridge     columns\n",
       "7     0.476180   [-0.0215599423722]      [0.280455665264]       atemp\n",
       "0     0.193429     [0.295134324615]      [0.275756076852]      season\n",
       "6     0.067640     [0.655629964983]      [0.350160925498]        temp\n",
       "1     0.000000  [-4.4408920985e-16]                 [0.0]          yr\n",
       "2    -0.000000    [-0.036923920106]     [-0.033893233126]     holiday\n",
       "3     0.000000    [0.0359658349529]     [0.0341799557374]     weekday\n",
       "4     0.000000   [0.00517147419455]    [0.00669579815907]  workingday\n",
       "8    -0.000000    [-0.059487568978]    [-0.0629670606878]         hum\n",
       "9    -0.000000    [-0.123935941959]     [-0.117141371205]   windspeed\n",
       "10    0.000000   [0.00394334912287]     [0.0188534422286]       month\n",
       "11    0.000000  [-0.00263300095055]  [-0.000768661515266]         day\n",
       "5    -0.127561    [-0.225692650157]     [-0.216428517895]  weathersit"
      ]
     },
     "execution_count": 46,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "mses = np.mean(lasso.mse_path_, axis = 1)\n",
    "plt.plot(np.log10(lasso.alphas_), mses)\n",
    "\n",
    "plt.xlabel('log(alpha)')\n",
    "plt.ylabel('mse')\n",
    "plt.show()\n",
    "\n",
    "print('alpha is:', lasso.alpha_)\n",
    "\n",
    "fs = pd.DataFrame({\"columns\": list(columns), \"coef_lr\":list((lr.coef_.T)),\"coef_ridge\":list((ridge.coef_.T)), \"coef_lasso\":list((lasso.coef_.T))})\n",
    "fs.sort_values(by=['coef_lasso'], ascending=False)\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "从lasso分析后发现atemp season temp是影响权重最高的三个特征，但在lasso分析当中，weekday 和workingday权重却很低。"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "lr ridge lasso模型分析出来的各特征权重有一定的参考性，也可能会与现实实际情况有很大的差异。如atemp season temp在三种模型中都体现了很好的效果，但weekday 等特征在其他模型中有着一一定的差异。"
   ]
  }
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